Image registration aims to spatially align one image to another. For that purpose, parameters of a global transformation model, such as rigid, affine or projective, are to be recovered to geometrically transform a moving image to achieve high spatial correspondence with a fixed image. The problem has been studied in various contexts due to its significance in a wide range of areas, including medical image fusion, remote sensing, recognition, tracking, mosaicing, and so on.
Rigid registration of 2D/3D medical images is a vital component of a large number of registration and fusion applications. In the areas of diagnosis, planning, evaluation of surgical and radio-therapeutical procedures, typically multiple single-modality, or multi-modality images are acquired in the clinical track of events. Since these images are complementary to each other, the integration of useful data from separate images are often desired. Rigid registration, a first step in this integration process, aims to bring the multiple images involved into spatial alignment.
Existing methods for image registration can largely be classified into three categories: feature-based methods, intensity-based methods, and hybrid methods that integrate the previous two. Feature-based methods use sparse geometric features such as points, curves, and/or surface patches, and their correspondences to compute an optimal transformation. These methods are relatively fast. However, the main critiques of this type of methods in the literature are the robustness of feature extraction, the accuracy of feature correspondences, and the frequent need of user interaction. Intensity-based registration methods operate directly on the intensity values from the full image content, without prior feature extraction. These methods have attracted much attention in recent years since they can be made fully automatic and can be used for multimodality image matching by utilizing appropriate similarity measures. However, these methods tend to have high computational cost due to the need for optimization on complex, non-convex energy functions. In addition, they require the poses of two input images be close enough to converge to a local optimum. Furthermore, they often perform poorly when partial matching is required.
Recently, several hybrid methods are proposed that integrate the merits of both feature-based and intensity-based methods. Most focus on incorporating user provided or automatically extracted geometric feature constraints into the intensity-based energy functionals to achieve smoother and faster optimization. Typically they are more flexible, and designed in such way that either intensity (gray values, gradients) information is incorporated into a feature-based algorithm, or feature (points, surfaces) information is introduced to a pixel/voxel intensity-based algorithm. The hybrid methods are expected to be more efficient and robust than the pure-feature, or pure-intensity based methods